Abstract: Systems and methods for training and using large language models to respond to queries and cascading to new models when their performance falls below a threshold during the response generation cycle are described. The methods involve receiving and analyzing a query using heuristics to determine the query's categories and a level of granularity at which its response is to be evaluated. Selecting a language model based on the query analysis and during the generation of the response, evaluating the model's performance for its ability to predict the next segment in a response. The methods score the evaluation and use the score to determine whether the language model's performance exceeds a confidence threshold. If it does not, then during the response is being generated, e.g., at a point before the response generation is completed, cascading from the model to a different model that can perform at a higher confidence level.

Abstract: A morphing interface system updates, that is, morphs a display on a client device as a user provides portions of input and additionally provides suggested selections for a user based on the received user input. The system receives a first portion of user input and generates intent suggestions for the user based on the user input. The intent suggestions, which represent predicted likely intents of the user, are provided to the user for selection. The user may select an intent suggestion or may provide additional user input. Based on the user response, the system determines whether an intent is selected or if additional information is needed. When an intent is selected, the interface morphs into an interface to provide predicted entity suggestions for the user to select entity values as inputs to execution of the intent.

Abstract: A morphing interface system updates, that is, morphs a display on a client device as a user provides portions of input and additionally provides suggested selections for a user based on the received user input. The system receives a first portion of user input and generates intent suggestions for the user based on the user input. The intent suggestions, which represent predicted likely intents of the user, are provided to the user for selection. The user may select an intent suggestion or may provide additional user input. Based on the user response, the system determines whether an intent is selected or if additional information is needed. When an intent is selected, the interface morphs into an interface to provide predicted entity suggestions for the user to select entity values as inputs to execution of the intent.

Abstract: A morphing interface system updates, that is, morphs a display on a client device as a user provides portions of input and additionally provides suggested selections for a user based on the received user input. The system receives a first portion of user input and generates intent suggestions for the user based on the user input. The intent suggestions, which represent predicted likely intents of the user, are provided to the user for selection. The user may select an intent suggestion or may provide additional user input. Based on the user response, the system determines whether an intent is selected or if additional information is needed. When an intent is selected, the interface morphs into an interface to provide predicted entity suggestions for the user to select entity values as inputs to execution of the intent.